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  <section id="module-causalai.application">
<span id="root-cause-detector-module"></span><h1>Root Cause Detector module<a class="headerlink" href="#module-causalai.application" title="Permalink to this heading"></a></h1>
<p>This module contains the two application APIs of causal discovery algorithms.</p>
<ul class="simple">
<li><p>RootCauseDetection: API for root cause detection.</p></li>
<li><p>TabularDistributionShiftDetector: API for root cause detection in tabular data.</p></li>
</ul>
<section id="module-causalai.application.root_cause_detection">
<span id="causalai-application-root-cause-detection"></span><h2>causalai.application.root_cause_detection<a class="headerlink" href="#module-causalai.application.root_cause_detection" title="Permalink to this heading"></a></h2>
<p>RootCauseDetector detects root cause of anomaly in continous time series data with 
the help of a higher-level context variable. The algorithm uses the PC algorithm to estimate the causal graph,
for root  cause analysis by treating the failure, represented using the higher-level metrics, as an intervention 
on the root cause node, and PC can use conditional independence tests to quickly detect 
which lower-level metric the failure node points to, as the root cause of anomaly.</p>
<p>This algorithm makes the following assumptions: 
1. observational samples conditioned on the higher-level context variable (e.g., time index) are i.i.d. 
2. linear relationship between variables with Gaussian noise terms,
3. Causal Markov condition, which implies that two variables that are d-separated in a causal graph are 
probabilistically independent
4. faithfulness, i.e., no conditional independence can hold unless the Causal Markov condition is met,
5. no hidden confounders.</p>
<dl class="py class">
<dt class="sig sig-object py" id="causalai.application.root_cause_detection.RootCauseDetector">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">causalai.application.root_cause_detection.</span></span><span class="sig-name descname"><span class="pre">RootCauseDetector</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_obj</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="data.tabular.html#causalai.data.tabular.TabularData" title="causalai.data.tabular.TabularData"><span class="pre">TabularData</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_metric_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'time'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prior_knowledge</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="models.common.prior_knowledge.html#causalai.models.common.prior_knowledge.PriorKnowledge" title="causalai.models.common.prior_knowledge.PriorKnowledge"><span class="pre">PriorKnowledge</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#causalai.application.root_cause_detection.RootCauseDetector" title="Permalink to this definition"></a></dt>
<dd><p>Detects root cause of distribution shift in time series data.</p>
<p>Reference: Ikram, Azam, et al. &quot;Root Cause Analysis of Failures in Microservices through Causal Discovery.&quot;
Advances in Neural Information Processing Systems 35 (2022): 31158-31170.</p>
<p>Reference: Huang, Biwei, et al. &quot;Causal discovery from heterogeneous/nonstationary data.&quot; 
The Journal of Machine Learning Research 21.1 (2020): 3482-3534.</p>
<dl class="py method">
<dt class="sig sig-object py" id="causalai.application.root_cause_detection.RootCauseDetector.__init__">
<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_obj</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="data.tabular.html#causalai.data.tabular.TabularData" title="causalai.data.tabular.TabularData"><span class="pre">TabularData</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_names</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_metric_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'time'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prior_knowledge</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="models.common.prior_knowledge.html#causalai.models.common.prior_knowledge.PriorKnowledge" title="causalai.models.common.prior_knowledge.PriorKnowledge"><span class="pre">PriorKnowledge</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#causalai.application.root_cause_detection.RootCauseDetector.__init__" title="Permalink to this definition"></a></dt>
<dd><p>PC algorithm for root cause detection in time-varying data settings.
:param data_obj: pre-processed TabularData object
:type data_obj: TabularData
:param var_names: list of variable names
:type var_names: List[str]
:param time_metric_name: name of the metric that represents time-varying context (e.g. time index)
:type time_metric_name: str</p>
<blockquote>
<div><p>Defaults to the name 'time'.</p>
</div></blockquote>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>prior_knowledge</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="models.common.prior_knowledge.html#causalai.models.common.prior_knowledge.PriorKnowledge" title="causalai.models.common.prior_knowledge.PriorKnowledge"><em>PriorKnowledge</em></a><em>]</em>) -- prior knowledge about the causal graph</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="causalai.application.root_cause_detection.RootCauseDetector.run">
<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pvalue_thres</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_condition_set_size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_graph</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#causalai.application.root_cause_detection.RootCauseDetector.run" title="Permalink to this definition"></a></dt>
<dd><p>Run the PC algorithm for root cause detection in microservice metrics.
:param pvalue_thres: p-value threshold for conditional independence test
:type pvalue_thres: float</p>
<blockquote>
<div><p>Defaults to 0.05.</p>
</div></blockquote>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>max_condition_set_size</strong> (<em>int
Defaults to 4.</em>) -- maximum size of the condition set</p></li>
<li><p><strong>return_graph</strong> (<em>bool
Defaults to False.</em>) -- whether to return the estimated causal graph</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>root cause of the incident and/or the estimated causal graph</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>Union[List[str], Dict[str, List[str]]]</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

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